Multi-step-ahead prediction of NOx emissions for a coal-based boiler

Until 2016 power plants within the EU will have to meet new limits on emissions as dictated by EU regulations. One of the major challenges is to reduce emissions of nitrogen oxides (NOx) due to health and ozone-formation concerns. Combustion optimisation is one, of the primary measures for reducing NOx emissions from boilers burning coal, oil, or natural gas. The optimisation can be achieved by excess air control, boiler fine tuning and balancing the fuel and air flow to the various burners in order to reach minimum NOx formation. In this paper, a multi-step-ahead prediction of NOx emissions that can provide a basis for on-line control is presented. About 9 days' worth of real data were acquired from an operator of a coal-based power plant for this study. It begins with a presentation of measured variables, pre-processing of the data and a definition of performance measures. Feature selection analysis follows, identifying the variables important for multi-step NOx prediction. In this respect, the impact of primary variables that are directly related to the combustion process is compared against that of other variables important in boiler operation and some transformed variables. Based on optimal features, a model comparison study including linear (ARX and ARMAX) and nonlinear (NN and SVR) modelling approaches is presented. Results of the model comparison study reveal that for the analysed boiler, nonlinear models do not improve the robust prediction performance of a linear ARX model. In the last part of the paper, an adaptive modelling approach further investigates the potential improvements in NOx prediction. A comparison of static and adaptive versions of the linear ARX model reveals that the adaptive approach does not improve prediction performance significantly. Hence the static ARX model in combination with an optimally selected set of input variables and extracted features is recommended for the multi-step NOx prediction of the coal-based boiler. (C) 2012 Published by Elsevier Ltd.